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SAP C_AIG_2412 Exam Syllabus Topics:

Topic	Details
Topic 1	SAP's Generative AI Hub: This section of the exam measures the skills of technology strategists and covers the functionalities provided by SAP's Generative AI Hub. It emphasizes how organizations can use generative AI to create new content and automate complex tasks. A vital skill evaluated is applying generative AI techniques to enhance business processes and customer experiences.
Topic 2	Large Language Models (LLMs): This section of the exam measures the skills of AI Developers and covers the evolution of large language models, distinguishing them from traditional IT operations analytics. It also explores the current stages of AIOps systems and their implications for organizations. A key skill assessed is understanding the foundational concepts behind LLMs and their applications in various contexts.
Topic 3	SAP Business AI: This section of the exam measures the skills of business analysts and covers the features and capabilities of SAP Business AI. It includes exploring how AI can automate processes, provide real-time insights, and enhance decision-making across various business functions.
Topic 4	SAP AI Core: This section of the exam measures the skills of SAP developers and covers the core components of SAP's AI framework. It emphasizes how these components integrate with existing systems to enhance functionality and performance. Leveraging SAP AI Core to develop intelligent applications that meet business needs is a critical skill that needs to be evaluated.

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SAP Certified Associate - SAP Generative AI Developer Sample Questions (Q52-Q57):

NEW QUESTION # 52
Which of the following must you do before connecting to a dataset in order to train a machine learning model in SAP Al Core?
Note: There are 2 correct answers to this question.

A. Grant access rights to the SAP BTP cockpit.
B. Store the dataset in the SAP HANA Vector Engine.
C. Provide the storage secret to access the dataset.
D. Store the dataset in a hyperscaler object store.

Answer: C,D

Explanation:
Before connecting to a dataset for training a machine learning model in SAP AI Core, the following steps are necessary:
* Store the dataset in a hyperscaler object store:Ensure that the dataset is stored in a compatible object storage service provided by a cloud hyperscaler (e.g., AWS S3, Azure Blob Storage) to facilitate seamless access during training.
* Provide the storage secret to access the dataset:Configure the necessary access credentials (storage secrets) within SAP AI Core to securely connect to and retrieve the dataset from the object store.
These steps are essential to establish a secure and efficient connection to the dataset, enabling successful model training within SAP AI Core.

NEW QUESTION # 53
You want to use the orchestration service through SAP's generative-Al-hub-sdk. What does the following code do?
from gen_ai_hub.orchestration.models.11m import LLM 11m =
LLM(name="gpt-40", version="latest", parameters={"max_tokens": 256, "temperature": 0.2})

A. Create the Orchestration Configuration
B. Define the Template and Default Input Values
C. Run the Orchestration Request
D. Define the LLM

Answer: D

Explanation:
The provided code snippet defines a Large Language Model (LLM) within the SAP Generative AI Hub SDK's orchestration service:
from gen_ai_hub.orchestration.models.llm import LLM
llm = LLM(name="gpt-40", version="latest", parameters={"max_tokens": 256, "temperature": 0.2})
1. Importing the LLM Class:
* Code:from gen_ai_hub.orchestration.models.llm import LLM
* Purpose:Imports the LLM class from the SDK, enabling the creation of an LLM instance.
2. Defining the LLM Instance:
* Code:llm = LLM(name="gpt-40", version="latest", parameters={"max_tokens": 256, "temperature":
0.2})
* Parameters:
* name:Specifies the model's name, in this case, "gpt-40".
* version:Indicates the model version, set to "latest" to use the most recent version.
* parameters:A dictionary defining model-specific parameters:
* max_tokens:Sets the maximum number of tokens (words or word pieces) the model can generate, here limited to 256 tokens.
* temperature:Controls the randomness of the output; a lower value like 0.2 results in more deterministic responses.
3. Role in Orchestration Pipeline:
* Function:This definition is a crucial step in the orchestration pipeline, specifying which LLM to use and configuring its behavior for subsequent tasks.
Conclusion:
The code snippet defines an LLM named "gpt-40" with specific parameters, preparing it for integration into an AI-driven workflow within SAP's Generative AI Hub.

NEW QUESTION # 54
What are some functionalities provided by SAP Al Core? Note: There are 3 correct answers to this question.

A. Integration of Al services with business applications using a standardized API
B. Orchestration of Al workflows such as model training and inference
C. Monitoring and retraining models in SAP Al Core
D. Continuous delivery and tenant isolation for scalability
E. Management of SAP S/4HANA cloud infrastructure

Answer: A,B,D

Explanation:
You're asking about the key functionalities of SAP AI Core. Here's a breakdown of the correct answers:
* A. Integration of AI services with business applications using a standardized API:SAP AI Core provides a standardized way to connect AI models and services to your existing business applications.
This means you can easily integrate AI capabilities into your core business processes, regardless of the specific AI technology you're using. This is done through APIs (Application Programming Interfaces), which allow different software systems to communicate with each other.
* B. Continuous delivery and tenant isolation for scalability:
* Continuous delivery:AI Core supports continuous delivery, which means you can quickly and easily deploy and update your AI models. This allows you to adapt to changing business needs and keep your AI solutions up-to-date.
* Tenant isolation:AI Core provides tenant isolation, which is important for security and scalability. This means that different users or departments within your organization can have their own separate AI environments, preventing interference and ensuring data privacy.
* C. Orchestration of AI workflows such as model training and inference:AI Core helps you manage the entire lifecycle of your AI models, including:
* Training:Automating the process of training your AI models on large datasets.
* Inference:Deploying your trained models and using them to make predictions or generate insights.
* Monitoring:Tracking the performance of your AI models over time.
Why the other options are incorrect:
* D. Management of SAP S/4HANA cloud infrastructure:While AI Core can be used with S/4HANA, it's not specifically designed to manage the cloud infrastructure of S/4HANA. That's handled by other SAP services.
* E. Monitoring and retraining models in SAP AI Core:While AI Core supports monitoring, the retraining of models is typically done using other tools and services within the SAP AI ecosystem.

NEW QUESTION # 55
What can be done once the training of a machine learning model has been completed in SAP AICore? Note:
There are 2 correct answers to this question.

A. The model's accuracy can be optimized directly in SAP HANA.
B. The model can be registered in the hyperscaler object store.
C. The model can be deployed in SAP HANA.
D. The model can be deployed for inferencing.

Answer: B,D

Explanation:
Once the training of a machine learning model has been completed in SAP AI Core, several post-training actions can be undertaken to operationalize and manage the model effectively.
1. Deploying the Model for Inferencing:
* Deployment Process:After training, the model can be deployed as a service to handle inference requests. This involves setting up a model server that exposes an endpoint for applications to send data and receive predictions.
* Integration:The deployed model can be integrated into business applications, enabling real-time decision-making based on the model's predictions.

NEW QUESTION # 56
Which statement best describes the Chain-of-Thought (COT) prompting technique?

A. Linking multiple Al models in sequence, where each model's output becomes the input for the next model in the chain.
B. Concatenating multiple related prompts to form a chain, guiding the model through sequential reasoning steps.
C. Connecting related concepts by having the LLM generate chains of ideas.
D. Writing a series of connected prompts creating a chain of related information.

Answer: B

Explanation:
Chain-of-Thought (CoT) prompting is a technique that involves concatenating multiple related prompts to guide a language model through a series of reasoning steps, leading to a final conclusion.
1. Structure of CoT Prompting:
* Sequential Reasoning:By breaking down a complex problem into a sequence of intermediate prompts, the model addresses each step methodically, enhancing its problem-solving capabilities.
* Logical Progression:Each prompt builds upon the previous one, ensuring a coherent flow of information that mirrors human logical reasoning.
2. Advantages of CoT Prompting:
* Enhanced Comprehension:This structured approach helps the model understand and process intricate tasks by focusing on one aspect at a time.
* Improved Accuracy:By guiding the model through detailed reasoning steps, CoT prompting reduces the likelihood of errors in the final output.

NEW QUESTION # 57
......

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C_AIG_2412 VALID EXAM TIPS | C_AIG_2412 LATEST EXAM

C_AIG_2412 Valid Exam Tips | C_AIG_2412 Latest Exam